a distribution of topics for each document.

In [1]} a topic is formally defined as a distribution over a fixed vocabulary of words.
These results are usually delivered in matrix format. Notably, let \(\mathbf {M}\)  be the topic-word distribution matrix of size \(\mathrm {K} \times N_{\mathcal {W}}\)  and \(\mathbf {Q}\)  be the
document-topic distribution matrix of size \(N_{\mathcal {D}}\times \mathrm {K}\) . Then, the entry \(\mathbf {M}_{k,i}\) , where \(k = 1,\dots K\)  and \(i = 1, \dots N_{\mathcal {W}}\) , is the weight
of the word \(w_i\)  within the topic \(k\) , whereas the entry \(\mathbf {Q}_{d,k}\) , where \(d = 1, \dots N_{\mathcal {D}}\) , is the proportion of topic \(k\)  in the document \(d\) .
For a visual interpretation of the these matrices we refer the reader to Figure REF .
Network Construction
In this section, we explain how to convert any topic obtained through LDA into a network. Basically, we aim at converting a weighted list of words into a weighted and directed network, that is a network where each edge has both a direction and a weight and every node has a weight. The ultimate goal is thus providing a way to define the adjacency matrix of the network of a chosen topic (see Appendix  for notation and definition of adjacency matrix). In other words, we need a way to gauge the direction and weight of the syntagmatic relation between any pair of words, represented by edges in the network.
As first stage, the LDA2Net method requires a data preparatory phase that involves collecting all bigrams present in the corpus and arranging them in a fashion akin to the matrix \(\mathbf {U}\) . That means, building a vocabulary for bigrams \(\mathcal {B}= \lbrace  b_{1}, \dots , b_{N_{\mathcal {B}}}\rbrace \) , where \(N_\mathcal {B} = |\mathcal {B}|\) . Unlike \(\mathcal {W}\) , this vocabulary contains unique ordered pairs of words. Then, bigram frequencies by document are collected into a matrix \(\mathbf {S}\) 
of size \(N_{\mathcal {B}} \times N_{\mathcal {D}}\) . Thus, each entry \(\mathbf {S}_{b,d}\)  represents the frequency of the bigram \(b\)  in the document \(d\) .
The next stage interests the computation of one of the two components of the LDA2Net-weights associated to a bigram, conditioned by the topic under analysis. This component is called \(counts\) -weight. Given a topic, a bigram will assume a \(counts\) -weight proportional to both its occurrence (counts) in documents and the proportion of the given topic in documents. This in turn means the same bigram may have a different \(counts\) -weight depending on the topic under investigation.
Formally, let \(\mathbf {C}\)  be the matrix of size \(N_\mathcal {B}\times K\)  collecting bigram \(counts\) -weights by topic; then each entry of \(\mathbf {C}\)  is computed as \(\mathbf {C}_{b,k} = \sum _d \mathbf {S}_{b,d}\, \mathbf {Q}_{d,k}\) . The same definition expressed for the whole matrix \(\mathbf {C}\)  is:
\(\mathbf {C} = \mathbf {S} \mathbf {Q}\) 
However, the quantities in
\(\mathbf {C}\)  do not take into account topic-specific probabilities of words composing the bigram, inferred through LDA. In fact, this information is given by \(\mathbf {M}\) , the topic-word distribution matrix.
For this reason, in the computation of the adjacency matrix of the network we combine the \(counts\) -weight with a second component, called \(probs\) -weight, which embeds these values. Specifically, let \(A^k\)  be the adjacency matrix describing the words network for the topic \(k\) , of size equal to \(N_\mathcal {W}\times N_\mathcal {W}\) . You may notice that, regardless of the topic under consideration, every network has the same node-set.
Nevertheless, topic networks are not identical as the LDA2Net-weights of edges between words, as well as the node weights, distinctively characterize each topic.
In this regard, let \(e_{i,j}^k\)  be
the LDA2Net-weight of the bigram made by the ordered pair of words \(w_i\)  and \(w_j\)  in the network representing topic \(k\) . Such a quantity exists if and only if the pair of words \((w_i,w_j)\)  is a bigram belonging to the vocabulary of (observed) bigrams \(\mathcal {B}\) .
Then, we define the adjacency matrix for the topic \(k\)  as:
\(A^k_{i,j}={\left\lbrace \begin{array}{ll}0, & \text{if}\ (w_i, w_j) \notin \mathcal {B} \\e_{i,j}^k, & \text{otherwise}\end{array}\right.}\) 
where \(e_{i,j}^k\)  is given by the product between the counts-weight of the bigram \((w_i, w_j)\)  for the topic \(k\) , that is \(\mathbf {C}_{b,k}\) , and its probs-weight, which is obtained by multiplying the LDA word probabilities of \(w_i \)  and \( w_j\) , which are respectively given by \(\mathbf {M}_{k,i}\)  and \(\mathbf {M}_{k,j}\) . As a result, we have that LDA2Net-weight of the bigram \(b=(w_i, w_j)\)  is:
\(e_{i,j}^k = \mathbf {C}_{b,k} \mathbf {M}_{k,i} \mathbf {M}_{k,j}\) 
In this respect, we would like point out that if the bigram composed by \((w_i, w_j)\)  does not exist, it does not imply the bigram \((w_j, w_i)\)  does not exist as well, i.e. \((w_i, w_j) \ne (w_j, w_i)\) . In fact, a bigram represents an oriented edge between two words.
Finally, we normalise \(A^k\)  entries, so that, for each \(k\) , the sum of all entries is equal to 1.

Topic Metrics
A set of metrics to characterize each topic has been constructed and employed to classify topics in groups.
These metrics are:

Topic Mean and Variance: respectively the average value \(\mu _k\)  and its variance \(\sigma ^{2}_k\)  of the probability of a topic \(k\)  to appear in the corpus, formally \(\mu _k = \sum _d \mathbf {Q}_{d,k}/N_{\mathcal {D}}\)  and \(\sigma ^2_k = \sum _d (\mathbf {Q}_{d,k}-\mu _k)^2/N_{\mathcal {D}}\)

JS Divergence: the Jensen–Shannon divergence (please see Appendix ) over the topic-bigram distributions obtained from
the two components of the edges' LDA2Net-weight for topic \(k\) , that is, between counts-weights (\(\mathbf {C}_{b,k}\) ) and probs-weights (\(\mathbf {M}_{k,i}\mathbf {M}_{k,i}\) )

Barrat clustering coefficient: the weighted version of the clustering coefficient for each topic network [2]} (please see Appendix )

The Barrat clustering coefficient, which has to do with the statistical level of cohesiveness of the topic's graph, measuring the global density of
interconnected vertex triplets in the network, and the Jensen–Shannon divergence, which informs us of the redundancy among the two components used to compute LDA2Net-weights, are in many aspects complementary to the topic mean and variance metrics.
As a result, these metrics can be jointly employed for clustering topics (in this work we employ a gaussian finate mixture model for this purpose). This allowed us to distinguish between cross-cutting and specialized topics (see Figure REF ).

Automatic Topic Community Labelling
One can leverage the edge and node metrics detailed in the Appendix REF  to create an heuristic capable of generating label propositions with desirable properties from random walks in topic networks or subtopic networks.
The heuristic here proposed for generating labels for (sub)topics using their network structure is summarised in Figure REF .
<FIGURE>Given the network \(\mathbf {G}_{m}^k\)  of a community \(m\)  of a topic \(k\) , a node is chosen to be the starting vertex of the random-walk.
The weighted out-degree centrality of nodes is used as the criterion to determine from which word (i.e., unigram) the random-walk is initialised.
This node is sampled from a discrete distribution for which the probability of node \(i\)  being extracted is proportional to its weighted out-degree, that is \(deg^{out}_{i}\) .
Given a topic community network \(\mathbf {G}_{m}^k= \mathbf {H}\) , we have that the probability \(Pr_{{i}_\mathbf {H}}\)  of a node \(i\)  to be selected is:
\(Pr_{{i}_\mathbf {H}}=\frac{deg^{out}_i}{\sum _{z}^{|\mathbf {H}|}deg^{out}_{z}}\) 
One of the advantages of the proposed heuristic initialisation step is that the resulting random-walks are more likely to start from nodes that are hubs from the point of view of the relevance of their outgoing edges, which is a good proxy for being a starting term in phrases related to a given topic or topic community.
Once a starting node has been sampled, for randomly moving from node to node, rather than using the edges' weights contained in
\({A}^k\) , one can exploit the edge betweenness centrality
of the latter. The reason for this is that the weighted betweenness centrality of edge (i,j), that is called \(btw_{(i,j)}\) , better captures the overall relevance of a bigram in a network, providing a global rather than local criterion for randomly moving from word to word.
Given the currently selected word \(i\) , the probability of moving from \(i\)  to \(j\)  is computed as follows:
\(Pr_{{(i,j)}_\mathbf {H}}= \frac{btw_{(i,j)}}{\sum _{z}^{|\mathbf {H}|}btw_{(i,z)}}\) 
In case none of the edges having \(i\)  as tail have a positive betweenness centrality, edge weights \(e_{i,j}\) , are used instead of the betweenness centrality \(btw_{(i,j)}\)  as the weighting criterion for randomly moving from node to node. If the selected word \(i\)  has no outgoing edges the random-walk is halted.
Given a desired n-gram label length \(l\) , through the aforementioned procedure one can generate thousands of random-walks (i.e., n-grams) of the same targeted length, then order them by relative frequency and use the most frequently sampled one(s) as label candidate(s) for a topic or a topic community (e.g., subtopic).

Acknowledgements
We thank Dr. Martina Gerotto for her expert support in topic labelling.
C.S. and M.W. acknowledge financial support from the European Union Horizon 2020 project ISEED (Grant Agreement No. 960366). C.S. acknowledges financial support from the European Union Horizon 2020 project MUHAI (Grant Agreement No. 951846).

Pre-processing
Only words that have more than two characters are considered in this work. Moreover, all abstracts that don't contain at least ten words that occur at least once every one million tokens have been removed. The resulting filtered corpus contains \(398,818\)  documents (i.e., article abstracts). The average number of terms per document is close to one hundred (\(98.21\) ) tokens, with a standard deviation of \(45.65\) .
Before tokenizing the corpus, uppercase strings referring to the abstract sections (e.g., ‘‘KEY RESULTS”) have been removed using a RegEx, as well as arabic and latin numbers. Also, English stopwords from the Quanteda [3]} R library have been identified through RegEx and removed from the documents' strings.
Excluding acronyms, all tokens have been lower-cased.
Terms that don't appear at least once every million terms have been removed, and therefore do not belong to the words vocabulary \(\mathcal {W}\) .
Remark that, in LDA2Net, tokenization of documents in bigrams takes place once the stopwords have been removed from the documents' strings. Also, during this process, all punctuation characters (except apostrophes and quotation marks) are considered string breaks, which means that two consecutive words that are separated by one or more punctuation characters (different from the aforementioned exceptions) will not be extracted and counted as a bigram.
Only the bigrams that are composed by two words (unigrams) contained in in the words vocabulary \(\mathcal {W}\)  are considered. By so doing, bigrams made up by very rare words are also filtered out.

Topic Modeling 
Topic modeling, in natural language processing, is a widely used technique aiming to automatically find themes (or topics) in text. Topic modeling can be performed by vector space models or Probabilistic Graphical Models. PGMs for topic modelling aim at discovering the latent semantic structures of a corpus by relying on a document generation process. The idea behind the document generation process comes from the human written articles. Indeed, when a person writes an article, she or he has some thinking in mind, some topics, and then she or he will extend these themes into some topic related words, which convey the desired meaning. Eventually, these words will be written down to complete an article. Probability topic models simulate the behavior of articles' generating process.
Latent Dirichlet
Allocation 
Latent Dirichlet
Allocation (LDA) as defined in [4]}, is a probabilistic generative model and draws upon this very idea. We define a generative model a machine learning technique generating an output considering the prior distribution of some objects.
LDA assigns a distribution of topics to each document, and a distribution of words to each topic to provide low dimensional, probabilistic descriptions of documents and words. In other words, LDA assumes that documents are mixtures of multiple topics, typically not many, and each document is generated by a process. Dirichlet Distributions encode the intuition that documents are related to a few topics. A topic, in turn, is a distribution over a fixed vocabulary and each topic is assumed to be generated first, before the documents. Only the number of topics is specified in advance.
In plain language, the generative process of a document takes two steps. First, a distribution over topics is chosen randomly (that implies a distribution over a distribution for this step). Then, for each word in the document, a topic from the distribution over topics is chosen randomly and next a word from the corresponding topic (distribution over the vocabulary) is picked randomly. Note that words are generated independently of other words (bag-of-words model).
Assuming this generative model for a collection of documents, LDA then tries to backtrack from the documents to find a set of topics that are likely to have generated the collection. In practical terms, given a set of documents, and the number of topics we would like to discover out of this set, the output will be the topic model, that is the documents expressed as a combination of the topics. The algorithm provides the weight of connections between documents and topics and between topics and words. Topic models are represented by top-m word lists for facilitating human interpretation.
For details on LDA refer to [4]}.

LDA parameters and model estimation
To select the number of topics \(K\)  for the LDA model, we employ the method proposed by [6]} and the R library ldatuning. Accordingly, for each \(K \in \lbrace 5,10,15,20,25,30,40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140\rbrace \)  we estimate 5 LDA models using using the Gibbs Sampling approach with different seeds and 1250 iterations (with \(n.burnin.iter=250\) ). Then, four criteria [7]}, [8]}, [9]}, [10]} are employed to choose the optimal value of \(K\) .
Based on the average values of the considered criteria, \(K=120\)  appears to be the a good candidate number of topics. Finally, to ensure convergence we resume the best run of the LDA estimation obtained for \(K=120\)  running 3000 additional iterations.

Network Theory Background
A network is a collection of vertices joined by edges. Vertices and edges are also called nodes and links in computer science. In mathematics, a network is called graph. A graph \(G = (V, E)\)  is defined as a set of vertices, \(V\) , which
are connected by a set of edges, \(E \in V\times V\) , typically represented as a square matrix. Given a graph \(G\)  with \(n\)  nodes, the adjacency matrix \(A = (a_i,a_j)\)  of \(G\)  is a square \(n\times n\)  matrix.
The elements \(A_{i,j} = a_{i,j}\)  of the adjacency matrix \(A\)  assume values
\(a_{i,j} \in \lbrace 0,1\rbrace \) , such that
\(A_{i,j}={\left\lbrace \begin{array}{ll}0, & \text{if}\ (i, j) \notin E \\1, & \text{otherwise}\end{array}\right.}\) 
that is \(a_{i,j}=1\)  if there exists an edge joining nodes \(i\)  and \(j\) , and \(a_{i,j}=0\)  otherwise.
A graph is undirected if edges have no direction. If there is an edge from \(i\)  to \(j\)  in an undirected graph, then there is also an edge from \(j\)  to \(i\) . This means that the adjacency matrix of an undirected graph is symmetric.
A graph is directed if edges have a direction. If there is an edge from \(i\)  to \(j\)  in an directed graph, then there is not necessarily an edge from \(j\)  to \(i\) , but it might exist. This means that the adjacency matrix of an directed graph is not necessarily symmetric.
For simple graphs without self-loops, the adjacency matrix has 0s on the diagonal.
Many of the networks we study are unweighted, that is, they have edges that form simple on/off connections between vertices. Either they are there or they are not. In some situations, however, it is useful to represent edges as having a strength, weight, or value to them. The weights in a weighted network are usually positive real numbers, but there is no reason in theory why they should not be negative. These weighted networks can be represented by giving the elements of the adjacency matrix values equal to the weights of the corresponding connections.
Network Measures
In order to characterize the structure of a graph, it is crucial to study and quantify its properties. These properties can be organized into three levels of abstraction:

element-level: to identify the most important nodes/links of the network

group-level: to find cohesive groups of nodes in the network

network-level: to study topological properties of networks as a whole

In network analysis, element-level properties are used to measure the level of importance of a single component of a graph wit respect to the others.
Element-level descriptors, also called centrality measures, are a crucial tool for understanding networks. These topological indicators, adopted to score both nodes and edges, are scalar values assigned to each node (edge) in the graph in order to quantify the node's (edge's) importance based on a certain assumption. In contrast,
network-level measurements, calculated over the whole network, provide overall indications about the network structure.
For our analysis, we take into account the following centralities:
Degree Centrality (node)
The degree centrality is the degree of a vertex, the number of edges connected to it. In directed networks, vertices have both an in-degree and an out-degree, and both may be useful as measures of centrality in the appropriate circumstances.

Betweenness Centrality (node)
The betweenness centrality [11]} over nodes measures the extent to which a vertex lies on paths between other vertices. This centrality detects the amount of influence a node has over the flow of information in a graph and thus it identifies nodes that serve as a bridge from one part of a graph to another. It is a measure based on shortest paths: for each vertex it is equal to the number of shortest (geodesic) paths that pass through the vertex.

PageRank (node)
The PageRank centrality [12]} is the trade name given it by the Google web search corporation, which has adopted it as key part of the web ranking technology. It is particularly suitable for directed network as it accounts for link direction. Each node in a network is assigned a score based on its indegree. These links are also weighted based on the relative score of its originating vertex. In this way nodes with many incoming links are influential, and nodes to which they are connected share some of that influence. PageRank can help uncover influential nodes whose reach extends beyond just their direct connections.

Betweenness Centrality (edge)
The betweenness centrality over edges [13]} is the sum of the fraction of all-pairs shortest paths that pass through that edge. Usually important bridge-like connectors between two parts of a network have high betweenness centrality as they have a large influence on the transfer of information through the network.

Clustering Coefficient in Weighted Structure (network) 
The clustering coefficient (CC) is a measure of the degree to which nodes in a graph tend to cluster together. There exist two versions of it:
a global version, designed to provide an overall indication of the clustering in the network and a local version that gives an idea of the embeddedness of single nodes.
In its global formulation, the clustering coefficient takes into account triplets of nodes. By triplet we mean three nodes that are connected by either two (open triplet) or three (closed triplet) undirected ties (see Figure REF ); in this context, a triangle is a three closed triplets, one centred on each of the nodes. The global clustering coefficient is the number of closed triplets (or 3 \(\times \)  triangles) over the total number of triplets (both open and closed).
A generalisation of the global clustering coefficient to weighted networks (networks with weighted edges) was proposed by [2]}. Here the authors first propose a novel definition of
the local clustering coefficient
- measuring the local cohesiveness considering both
the importance of the clustered structure and the interaction intensity (e.g., weights) actually found on the local triplets - and then
the global version, the weighted clustering coefficient
averaged over all vertices.
<FIGURE>
Jensen-Shannon Divergence 
The Jensen-Shannon divergence (JSD) is a measure assessing the difference between probability distributions, that is the ground truth and the simulation, by calculating the mutual information between two probability distributions, quantified by gauging the difference between entropies associated with those probability distributions. The JSD is a symmetrized and smoothed version of the Kullback-Liebler divergence.
In other words, it is a mutual information measure for assessing the similarity between two probability distributions. It is defined as
\(D_{J S}(P, Q)=\frac{1}{2} D_{K L}(P \Vert  M)+\frac{1}{2} D_{K L}(M \Vert  Q)\) 
where \(D_{K L}\)  is the classical Kullback-Leibler divergence and \( M =\frac{P +Q}{2}\) .
The generalization of the Jensen-Shannon divergence, in case of more than two probability distributions, is based on the Shannon entropy as follows
\(\begin{aligned}D_{{J S}_{\pi _{1}, \ldots , \pi _{n}}}\left(P_{1}, P_{2}, \ldots , P_{n}\right) &=\sum _{i} \pi _{i} D\left(P_{i} \Vert  M\right) \\&=H\left(\sum _{i=1}^{n} \pi _{i} P_{i}\right)-\sum _{i=1}^{n} \pi _{i} H\left(P_{i}\right)\end{aligned}\) 
where \({\pi _{1}, \ldots , \pi _{n}}\)  are the weights of the
probability distributions, \(M=\sum _{i=1}^{n} \pi _{i} P_{i}\)  and \(H(P)\)  is the Shannon entropy for distribution \(P\) .
When using the base 2 logarithm, the Jensen–Shannon divergence for two probability distributions ranges between 0 and 1 [15]}.

Community Detection
In the social context, a community is a set of individuals such that those within a group (also referred to as cluster, cohesive subgroup or module) interact with each other more frequently than with those outside the group. In this regard community detection is discovering groups where individuals’ group memberships are not explicitly given. Similarly, in network analysis, a community is a group of nodes, which are highly connected to each other than to the rest of the nodes in the network [16]}.
In the last years, community structure has increasingly become the
most-studied structural feature of complex networks.
The approach basically consists in dividing a network into subgroups by grouping nodes that are tightly coupled to each other and loosely coupled to
the rest of the vertices in the network [17]}.
However, the task of gauging the intuitive concept of community structure is not trivial.
One of the most effective approaches to this issue has been defining a quality function able to estimate
the strength of division of a network into modules [18]}, [17]}. This strength has been quantified by using the measure known as modularity [20]}, computed as the fraction of the edges that fall within the given groups minus the expected value of the fraction if edges were distributed at random (for more details we refer the reader to [21]}).
Although modularity optimization is an NP-hard problem, many community detection algorithms are based on this principle. The most famous ones include the greedy method of Newman and its fast version [22]}, the Louvain algorithm [23]} and the
method of Clauset, Newman, and Moore (CNM) [24]}.
Another approach to identify communities is by simulating random walks inside the network.
The general idea is that, given a graph and a starting point, if we select a
neighbour at random and move to the selected neighbour and repeat the same
process till a termination condition, this walk, namely a random sequence of nodes,
is more likely to stay within the same community as there are only a few edges that lead outside a given community. In other words, a random walker will tend to wander inside densely connected areas of the graph. This concepts inspired the Walktrap algorithm [25]} and the Infomap algorithm [26]}. Walktrap makes use of short random walks (of 3-4-5 steps) to compute distances between nodes.
Based on (small) intra and (larger) inter-community distances, nodes are being assigned to groups, via bottom-up hierarchical clustering. In this respect, modularity score can be used to select where to cut the dendrogram. It should be pointed out that this algorithm considers only one community per node, which in some cases can be an incorrect hypothesis.

Topic Labels Table
[ht]c|l|l|l|l
Topic #  Human label  2-gram label  3-gram label  4-gram label
1  protective devices  protection\(\rightarrow \)  equipment  protection\(\rightarrow \)  equipment\(\rightarrow \)  used  filtration\(\rightarrow \)  protection\(\rightarrow \)  equipment\(\rightarrow \)  used
2  diagnostics  method\(\rightarrow \)  developed  method\(\rightarrow \)  developed\(\rightarrow \)  used  method\(\rightarrow \)  developed\(\rightarrow \)  rapid\(\rightarrow \)  sensitive
3  statistical analysis (studies)  analysis\(\rightarrow \)  used  data\(\rightarrow \)  analysis\(\rightarrow \)  used  data\(\rightarrow \)  analysis\(\rightarrow \)  used\(\rightarrow \)  analyzed
4  mortality rate related to infection  number\(\rightarrow \)  cases  number\(\rightarrow \)  cases\(\rightarrow \)  deaths  number\(\rightarrow \)  cases\(\rightarrow \)  deaths\(\rightarrow \)  per
5  epidemic models  mathematical\(\rightarrow \)  model  epidemic\(\rightarrow \)  used\(\rightarrow \)  model  reproduction\(\rightarrow \)  number\(\rightarrow \)  R0\(\rightarrow \)  model
6  retrospective clinical study  hospitalized\(\rightarrow \)  confirmed  hospitalized\(\rightarrow \)  confirmed\(\rightarrow \)  without  hospitalized\(\rightarrow \)  confirmed\(\rightarrow \)  without\(\rightarrow \)  diagnosed
7  literature review  various\(\rightarrow \)  aspects  several\(\rightarrow \)  specific\(\rightarrow \)  aspects  aspects\(\rightarrow \)  human\(\rightarrow \)  papillomavirus\(\rightarrow \)  related
8  clinical studies on illness development  mortality\(\rightarrow \)  rate  mortality\(\rightarrow \)  rate\(\rightarrow \)  patients  mortality\(\rightarrow \)  rate\(\rightarrow \)  patients\(\rightarrow \)  risk
9  guidelines for clinical practice management  clinical\(\rightarrow \)  practice  clinical\(\rightarrow \)  practice\(\rightarrow \)  management  clinical\(\rightarrow \)  practice\(\rightarrow \)  management\(\rightarrow \)  regarding
10  statistical analysis (scale assessment)  scale\(\rightarrow \)  scores  scale\(\rightarrow \)  scores\(\rightarrow \)  used  mean\(\rightarrow \)  score\(\rightarrow \)  scale\(\rightarrow \)  scores
11  cellular immune response  inflammatory\(\rightarrow \)  macrophages  inflammatory\(\rightarrow \)  macrophages\(\rightarrow \)  including  inflammatory\(\rightarrow \)  loop\(\rightarrow \)  inflammation\(\rightarrow \)  production
12  online education during pandemic  online\(\rightarrow \)  learning  online\(\rightarrow \)  learning\(\rightarrow \)  students  online\(\rightarrow \)  de\(\rightarrow \)  student\(\rightarrow \)  learning
13  studies on origins  province\(\rightarrow \)  since  province\(\rightarrow \)  city\(\rightarrow \)  china  province\(\rightarrow \)  since\(\rightarrow \)  prevention\(\rightarrow \)  february
14  public health response to pandemics  public\(\rightarrow \)  system  public\(\rightarrow \)  system\(\rightarrow \)  crisis  public\(\rightarrow \)  system\(\rightarrow \)  crisis\(\rightarrow \)  health
15  reviews on recent therapeutical approaches  therapeutic\(\rightarrow \)  approaches  future\(\rightarrow \)  strategies\(\rightarrow \)  various  future\(\rightarrow \)  strategies\(\rightarrow \)  various\(\rightarrow \)  approaches
16  mechanism of cell infection  ACE2\(\rightarrow \)  binding  ACE2\(\rightarrow \)  binding\(\rightarrow \)  expression  receptor\(\rightarrow \)  ACE2\(\rightarrow \)  binding\(\rightarrow \)  expression
17  ND  important\(\rightarrow \)  key  important\(\rightarrow \)  key\(\rightarrow \)  critical  important\(\rightarrow \)  key\(\rightarrow \)  critical\(\rightarrow \)  crucial
18  study of variants  viral\(\rightarrow \)  genome  genome\(\rightarrow \)  sequences\(\rightarrow \)  identified  viral\(\rightarrow \)  genome\(\rightarrow \)  sequencing\(\rightarrow \)  sequence
19  management of surgical procedures  surgical\(\rightarrow \)  procedures  surgical\(\rightarrow \)  procedures\(\rightarrow \)  patients  surgical\(\rightarrow \)  procedures\(\rightarrow \)  patients\(\rightarrow \)  procedure
20  instrumental diagnosis  chest\(\rightarrow \)  CT  chest\(\rightarrow \)  CT\(\rightarrow \)  scan  chest\(\rightarrow \)  CT\(\rightarrow \)  scan\(\rightarrow \)  images
21  contagion diffusion control  contact\(\rightarrow \)  tracing  contact\(\rightarrow \)  tracing\(\rightarrow \)  individuals  contact\(\rightarrow \)  tracing\(\rightarrow \)  persons\(\rightarrow \)  quarantine
22  impact of pandemics on people (social relations)  social\(\rightarrow \)  distancing  social\(\rightarrow \)  distancing\(\rightarrow \)  isolation  social\(\rightarrow \)  distancing\(\rightarrow \)  isolation\(\rightarrow \)  support
23  virus Cov-19 infection  virus\(\rightarrow \)  human  virus\(\rightarrow \)  human\(\rightarrow \)  viruses  SARS\(\rightarrow \)  MERS\(\rightarrow \)  virus\(\rightarrow \)  human
24  pandemics history  help\(\rightarrow \)  us  help\(\rightarrow \)  us\(\rightarrow \)  make  help\(\rightarrow \)  us\(\rightarrow \)  make\(\rightarrow \)  one
25  comparatiove analysis of viremic levels  significantly\(\rightarrow \)  higher  levels\(\rightarrow \)  significantly\(\rightarrow \)  higher  significantly\(\rightarrow \)  higher\(\rightarrow \)  lower\(\rightarrow \)  level
26  access to health of socio-economic groups  household\(\rightarrow \)  income  household\(\rightarrow \)  income\(\rightarrow \)  inequality  population\(\rightarrow \)  derived\(\rightarrow \)  nonprobability\(\rightarrow \)  sample
27  Studies, evidences  evidence\(\rightarrow \)  suggest  evidence\(\rightarrow \)  suggest\(\rightarrow \)  studies  evidence\(\rightarrow \)  suggests\(\rightarrow \)  studies\(\rightarrow \)  needed
28  methodological approaches  approach\(\rightarrow \)  problem  set\(\rightarrow \)  theory\(\rightarrow \)  method  approach\(\rightarrow \)  problem\(\rightarrow \)  time\(\rightarrow \)  algorithm
29  ND  research\(\rightarrow \)  limitations  research\(\rightarrow \)  limitations\(\rightarrow \)  findings  research\(\rightarrow \)  limitations\(\rightarrow \)  development\(\rightarrow \)  process
30  development in India  urban\(\rightarrow \)  population  urban\(\rightarrow \)  population\(\rightarrow \)  area  urban\(\rightarrow \)  population\(\rightarrow \)  planning\(\rightarrow \)  space
31  digital technology  digital\(\rightarrow \)  version  digital\(\rightarrow \)  scanning\(\rightarrow \)  calorimetry  digital\(\rightarrow \)  scanning\(\rightarrow \)  calorimetry\(\rightarrow \)  technology
32  Covid-19 literature  must\(\rightarrow \)  considered  one\(\rightarrow \)  especially\(\rightarrow \)  important  one\(\rightarrow \)  especially\(\rightarrow \)  important\(\rightarrow \)  reasons
33  abstract structure  methods\(\rightarrow \)  results  methods\(\rightarrow \)  results\(\rightarrow \)  study  materials\(\rightarrow \)  methods\(\rightarrow \)  results\(\rightarrow \)  study
34  prevention  prevention\(\rightarrow \)  measures  control\(\rightarrow \)  prevention\(\rightarrow \)  measures  prevention\(\rightarrow \)  measures\(\rightarrow \)  spread\(\rightarrow \)  strategies
35  review studies  systematic\(\rightarrow \)  review  systematic\(\rightarrow \)  reviews\(\rightarrow \)  protocol  systematic\(\rightarrow \)  review\(\rightarrow \)  met\(\rightarrow \)  literature
36  economic impact  tourism\(\rightarrow \)  development  tourism\(\rightarrow \)  development\(\rightarrow \)  business  tourism\(\rightarrow \)  development\(\rightarrow \)  business\(\rightarrow \)  market
37  comparative studies  significantly\(\rightarrow \)  higher  significantly\(\rightarrow \)  higher\(\rightarrow \)  rate  significantly\(\rightarrow \)  higher\(\rightarrow \)  rate\(\rightarrow \)  compared
38  effects of vitamin somministration  levels\(\rightarrow \)  supplementation  levels\(\rightarrow \)  supplementation\(\rightarrow \)  significantly  levels\(\rightarrow \)  supplementation\(\rightarrow \)  significantly\(\rightarrow \)  reduced
39  data  data\(\rightarrow \)  collection  data\(\rightarrow \)  collection\(\rightarrow \)  analysis  data\(\rightarrow \)  collection\(\rightarrow \)  sources\(\rightarrow \)  used
40  publication metadata  without\(\rightarrow \)  use  copyright\(\rightarrow \)  international\(\rightarrow \)  use  copyright\(\rightarrow \)  international\(\rightarrow \)  use\(\rightarrow \)  abstract
41  impact on life quality & sexual health  quality\(\rightarrow \)  life  quality\(\rightarrow \)  life\(\rightarrow \)  expectancy  quality\(\rightarrow \)  life\(\rightarrow \)  expectancy\(\rightarrow \)  health
42  symptoms & clinical manifestations  clinical\(\rightarrow \)  symptoms  gastrointestinal\(\rightarrow \)  infection\(\rightarrow \)  symptoms  clinical\(\rightarrow \)  symptoms\(\rightarrow \)  manifestations\(\rightarrow \)  reported
43  effects of alcohool & addictions  alcohol\(\rightarrow \)  medications  alcohol\(\rightarrow \)  medications\(\rightarrow \)  treatment  alcohol\(\rightarrow \)  medications\(\rightarrow \)  treatment\(\rightarrow \)  cannabis
44  effects on patients with comorbidity  severe\(\rightarrow \)  disease  patients\(\rightarrow \)  severe\(\rightarrow \)  disease  patients\(\rightarrow \)  severe\(\rightarrow \)  disease\(\rightarrow \)  critical
45  impact of air pollution  concentration\(\rightarrow \)  meteorological  concentration\(\rightarrow \)  meteorological\(\rightarrow \)  conditions  concentration\(\rightarrow \)  meteorological\(\rightarrow \)  conditions\(\rightarrow \)  observed
46  correlation with heart disease  heart\(\rightarrow \)  failure  cardiac\(\rightarrow \)  arrest\(\rightarrow \)  patients  heart\(\rightarrow \)  failure\(\rightarrow \)  arrhythmias\(\rightarrow \)  acute
47  ND  new\(\rightarrow \)  york  new\(\rightarrow \)  york\(\rightarrow \)  city  new\(\rightarrow \)  york\(\rightarrow \)  city\(\rightarrow \)  pandemic
48  community  community\(\rightarrow \)  engagement  community\(\rightarrow \)  engagement\(\rightarrow \)  development  sustainable\(\rightarrow \)  strategies\(\rightarrow \)  partnerships\(\rightarrow \)  key
49  stages  early\(\rightarrow \)  especially  early\(\rightarrow \)  especially\(\rightarrow \)  initial  early\(\rightarrow \)  especially\(\rightarrow \)  initial\(\rightarrow \)  results
50  ND  higher\(\rightarrow \)  risk  adjusted\(\rightarrow \)  prevalence\(\rightarrow \)  ratio  adjusted\(\rightarrow \)  prevalence\(\rightarrow \)  ratio\(\rightarrow \)  among
51  age and gender  older\(\rightarrow \)  people  older\(\rightarrow \)  people\(\rightarrow \)  individuals  older\(\rightarrow \)  people\(\rightarrow \)  individuals\(\rightarrow \)  higher
52  interview studies  themes\(\rightarrow \)  emerged  themes\(\rightarrow \)  emerged\(\rightarrow \)  three  themes\(\rightarrow \)  emerged\(\rightarrow \)  three\(\rightarrow \)  key
53  diagnostic tests efficacy  test\(\rightarrow \)  positive  test\(\rightarrow \)  positive\(\rightarrow \)  results  test\(\rightarrow \)  positive\(\rightarrow \)  results\(\rightarrow \)  tests
54  french  de\(\rightarrow \)  patients  de\(\rightarrow \)  la\(\rightarrow \)  santé  de\(\rightarrow \)  la\(\rightarrow \)  santé\(\rightarrow \)  plus
55  pandemic management  pandemic\(\rightarrow \)  response  pandemic\(\rightarrow \)  response\(\rightarrow \)  crisis  pandemic\(\rightarrow \)  response\(\rightarrow \)  crisis\(\rightarrow \)  management
56  nursing programs residents  programs\(\rightarrow \)  program  programs\(\rightarrow \)  program\(\rightarrow \)  value  programs\(\rightarrow \)  program\(\rightarrow \)  value\(\rightarrow \)  virtual
57  effective strategies  effective\(\rightarrow \)  strategies  effective\(\rightarrow \)  strategies\(\rightarrow \)  strategy  effective\(\rightarrow \)  strategies\(\rightarrow \)  strategy\(\rightarrow \)  approach
58  policies for spread containment  control\(\rightarrow \)  measures  containment\(\rightarrow \)  interventions\(\rightarrow \)  implemented  containment\(\rightarrow \)  interventions\(\rightarrow \)  implemented\(\rightarrow \)  reduce
59  technologies  surface\(\rightarrow \)  structure  process\(\rightarrow \)  storage\(\rightarrow \)  efficiency  process\(\rightarrow \)  storage\(\rightarrow \)  parameters\(\rightarrow \)  ph
60  susceptibility to viral infection in epathitys  viral\(\rightarrow \)  infection  viral\(\rightarrow \)  infection\(\rightarrow \)  infections  viral\(\rightarrow \)  infection\(\rightarrow \)  infections\(\rightarrow \)  caused
61  data for the study  analysis\(\rightarrow \)  used  data\(\rightarrow \)  analysis\(\rightarrow \)  used  data\(\rightarrow \)  analysis\(\rightarrow \)  used\(\rightarrow \)  study
62  sociopolitical aspects  political\(\rightarrow \)  cultural  political\(\rightarrow \)  questions\(\rightarrow \)  social  ethical\(\rightarrow \)  issues\(\rightarrow \)  legal\(\rightarrow \)  political
63  cancer therapy  cancer\(\rightarrow \)  treatment  cancer\(\rightarrow \)  treatment\(\rightarrow \)  patients  cancer\(\rightarrow \)  patients\(\rightarrow \)  diagnosis\(\rightarrow \)  therapy
64  pediatry  children\(\rightarrow \)  adolescents  children\(\rightarrow \)  adolescents\(\rightarrow \)  young  children\(\rightarrow \)  adolescents\(\rightarrow \)  young\(\rightarrow \)  families
65  oftalmic  ocular\(\rightarrow \)  surface  eye\(\rightarrow \)  ocular\(\rightarrow \)  surface  ocular\(\rightarrow \)  surface\(\rightarrow \)  conditions\(\rightarrow \)  presence
66  comorbidity diabetes  chronic\(\rightarrow \)  diseases  chronic\(\rightarrow \)  diseases\(\rightarrow \)  disease  chronic\(\rightarrow \)  diseases\(\rightarrow \)  disease\(\rightarrow \)  diabetes
67  rehabilitation after intervention  intervention\(\rightarrow \)  program  intervention\(\rightarrow \)  program\(\rightarrow \)  improve  intervention\(\rightarrow \)  program\(\rightarrow \)  improve\(\rightarrow \)  organizational
68  pandemics in the us  state\(\rightarrow \)  local  state\(\rightarrow \)  local\(\rightarrow \)  federal  state\(\rightarrow \)  local\(\rightarrow \)  federal\(\rightarrow \)  level
69  immunity & specificity of response  igg\(\rightarrow \)  antibodies  igg\(\rightarrow \)  antibodies\(\rightarrow \)  antibody  igg\(\rightarrow \)  antibodies\(\rightarrow \)  antibody\(\rightarrow \)  response
70  online (mis)information  information\(\rightarrow \)  sources  information\(\rightarrow \)  sources\(\rightarrow \)  related  information\(\rightarrow \)  sources\(\rightarrow \)  regarding\(\rightarrow \)  SDM
71  variability  three\(\rightarrow \)  different  different\(\rightarrow \)  patterns\(\rightarrow \)  populations  three\(\rightarrow \)  different\(\rightarrow \)  patterns\(\rightarrow \)  populations
72  healthcare  health\(\rightarrow \)  facilities  health\(\rightarrow \)  facilities\(\rightarrow \)  system  health\(\rightarrow \)  facilities\(\rightarrow \)  system\(\rightarrow \)  planning
73  paper info  web\(\rightarrow \)  response  web\(\rightarrow \)  response\(\rightarrow \)  one  web\(\rightarrow \)  response\(\rightarrow \)  disclosure\(\rightarrow \)  including
74  clinical cases reports  patient\(\rightarrow \)  presented  patient\(\rightarrow \)  presented\(\rightarrow \)  developed  patient\(\rightarrow \)  presented\(\rightarrow \)  developed\(\rightarrow \)  acute
75  numbers  one\(\rightarrow \)  three  one\(\rightarrow \)  three\(\rightarrow \)  study  one\(\rightarrow \)  three\(\rightarrow \)  study\(\rightarrow \)  five
76  UK pandemic timeline  first\(\rightarrow \)  time  first\(\rightarrow \)  time\(\rightarrow \)  UK  first\(\rightarrow \)  time\(\rightarrow \)  UK\(\rightarrow \)  weeks
77  time & duration  median\(\rightarrow \)  time  median\(\rightarrow \)  time\(\rightarrow \)  period  median\(\rightarrow \)  time\(\rightarrow \)  period\(\rightarrow \)  day
78  geographic (countries)  world\(\rightarrow \)  health  around\(\rightarrow \)  world\(\rightarrow \)  health  around\(\rightarrow \)  world\(\rightarrow \)  health\(\rightarrow \)  response
79  impact (on energy market)  paper\(\rightarrow \)  shows  paper\(\rightarrow \)  shows\(\rightarrow \)  impact  paper\(\rightarrow \)  shows\(\rightarrow \)  impact\(\rightarrow \)  examine
80  antiviral treatments  antiviral\(\rightarrow \)  drugs  antiviral\(\rightarrow \)  drugs\(\rightarrow \)  used  antiviral\(\rightarrow \)  drugs\(\rightarrow \)  used\(\rightarrow \)  treatment
81  medical organization and management  emergency\(\rightarrow \)  medicine  medical\(\rightarrow \)  personnel\(\rightarrow \)  emergency  emergency\(\rightarrow \)  medicine\(\rightarrow \)  doctors\(\rightarrow \)  management
82  mental health  psychological\(\rightarrow \)  stress  stress\(\rightarrow \)  disorder\(\rightarrow \)  symptoms  psychological\(\rightarrow \)  stress\(\rightarrow \)  disorder\(\rightarrow \)  symptoms
83  clinical studies on illness development  clinical\(\rightarrow \)  trial  clinical\(\rightarrow \)  trial\(\rightarrow \)  outcomes  clinical\(\rightarrow \)  trial\(\rightarrow \)  outcomes\(\rightarrow \)  registered
84  questionnaire studies  study\(\rightarrow \)  participants  study\(\rightarrow \)  participants\(\rightarrow \)  conducted  study\(\rightarrow \)  participants\(\rightarrow \)  conducted\(\rightarrow \)  aimed
85  damage mechansims  nervous\(\rightarrow \)  system  nervous\(\rightarrow \)  system\(\rightarrow \)  involvement  nervous\(\rightarrow \)  system\(\rightarrow \)  damage\(\rightarrow \)  including
86  anti-inflammatory treatment  patients\(\rightarrow \)  received  patients\(\rightarrow \)  received\(\rightarrow \)  treatment  treatment\(\rightarrow \)  receiving\(\rightarrow \)  tocilizumab\(\rightarrow \)  systemic
87  effects if pandemic  pandemic\(\rightarrow \)  impact  pandemic\(\rightarrow \)  impact\(\rightarrow \)  affected  pandemic\(\rightarrow \)  impact\(\rightarrow \)  affected\(\rightarrow \)  changes
88  antiviral drug molecules  drug\(\rightarrow \)  target  antiviral\(\rightarrow \)  activity\(\rightarrow \)  compounds  antiviral\(\rightarrow \)  targets\(\rightarrow \)  identified\(\rightarrow \)  compounds
89  risk factors  risk\(\rightarrow \)  factors  risk\(\rightarrow \)  factor\(\rightarrow \)  exposure  risk\(\rightarrow \)  factor\(\rightarrow \)  exposure\(\rightarrow \)  factors
90  neurological and cognitive impairment  brain\(\rightarrow \)  including  neurologic\(\rightarrow \)  manifestations\(\rightarrow \)  including  neurologic\(\rightarrow \)  manifestations\(\rightarrow \)  including\(\rightarrow \)  brain
91  respiratory failure  patients\(\rightarrow \)  severe  patients\(\rightarrow \)  severe\(\rightarrow \)  forms  patients\(\rightarrow \)  local\(\rightarrow \)  contestation\(\rightarrow \)  different
92  predictive models  predictive\(\rightarrow \)  value  predictive\(\rightarrow \)  value\(\rightarrow \)  model  predictive\(\rightarrow \)  value\(\rightarrow \)  model\(\rightarrow \)  used
93  behavioural studies  behavior\(\rightarrow \)  intention  positive\(\rightarrow \)  relationship\(\rightarrow \)  perceived  positive\(\rightarrow \)  relationship\(\rightarrow \)  perceived\(\rightarrow \)  behavioral
94  sampling viral rna  viral\(\rightarrow \)  RNA  viral\(\rightarrow \)  RNA\(\rightarrow \)  load  viral\(\rightarrow \)  RNA\(\rightarrow \)  loads\(\rightarrow \)  samples
95  pandemic impact  current\(\rightarrow \)  pandemic  current\(\rightarrow \)  pandemic\(\rightarrow \)  caused  current\(\rightarrow \)  pandemic\(\rightarrow \)  caused\(\rightarrow \)  coronavirus
96  orthopedic traumas  wound\(\rightarrow \)  healing  wound\(\rightarrow \)  healing\(\rightarrow \)  repair  wound\(\rightarrow \)  healing\(\rightarrow \)  repair\(\rightarrow \)  attempt
97  vaccine and immunity  vaccine\(\rightarrow \)  development  vaccine\(\rightarrow \)  development\(\rightarrow \)  efficacy  vaccine\(\rightarrow \)  development\(\rightarrow \)  candidate\(\rightarrow \)  vaccines
98  icu and patinet care  ICU\(\rightarrow \)  patients  care\(\rightarrow \)  unit\(\rightarrow \)  patients  patients\(\rightarrow \)  admitted\(\rightarrow \)  hospitalized\(\rightarrow \)  hospital
99  cellular mechanisms  gene\(\rightarrow \)  expression  gene\(\rightarrow \)  expression\(\rightarrow \)  genes  gene\(\rightarrow \)  expression\(\rightarrow \)  genes\(\rightarrow \)  involved
100  pandemic  coronavirus\(\rightarrow \)  disease  novel\(\rightarrow \)  coronavirus\(\rightarrow \)  disease  coronavirus\(\rightarrow \)  disease\(\rightarrow \)  virus\(\rightarrow \)  spread
101  health care personnel and pandemic  healthcare\(\rightarrow \)  workers  healthcare\(\rightarrow \)  workers\(\rightarrow \)  work  healthcare\(\rightarrow \)  workers\(\rightarrow \)  work\(\rightarrow \)  providers
102  transplant  dental\(\rightarrow \)  treatment  clinical\(\rightarrow \)  course\(\rightarrow \)  dental  clinical\(\rightarrow \)  course\(\rightarrow \)  dental\(\rightarrow \)  treatment
103  structural dynamics  complex\(\rightarrow \)  interactions  complex\(\rightarrow \)  interactions\(\rightarrow \)  structure  complex\(\rightarrow \)  interactions\(\rightarrow \)  structure\(\rightarrow \)  dynamics
104  treatment of cardiovascular complications  stroke\(\rightarrow \)  acute  stroke\(\rightarrow \)  acute\(\rightarrow \)  severe  ischemic\(\rightarrow \)  thrombotic\(\rightarrow \)  outcomes\(\rightarrow \)  complications
105  pandemic in selected countires  italy\(\rightarrow \)  spain  italy\(\rightarrow \)  spain\(\rightarrow \)  one  italy\(\rightarrow \)  spain\(\rightarrow \)  one\(\rightarrow \)  france
106  spanish  de\(\rightarrow \)  la  de\(\rightarrow \)  la\(\rightarrow \)  pandemia  de\(\rightarrow \)  la\(\rightarrow \)  pandemia\(\rightarrow \)  esta
107  AI models  deep\(\rightarrow \)  learning  deep\(\rightarrow \)  learning\(\rightarrow \)  de  deep\(\rightarrow \)  learning\(\rightarrow \)  de\(\rightarrow \)  performance
108  physical activity and exercise  home\(\rightarrow \)  time  home\(\rightarrow \)  time\(\rightarrow \)  confinement  home\(\rightarrow \)  time\(\rightarrow \)  confinement\(\rightarrow \)  le
109  statistics  control\(\rightarrow \)  groups  control\(\rightarrow \)  groups\(\rightarrow \)  significant  control\(\rightarrow \)  groups\(\rightarrow \)  significant\(\rightarrow \)  difference
110  animal models & in vitro  stem\(\rightarrow \)  reduced  stem\(\rightarrow \)  reduced\(\rightarrow \)  lung  stem\(\rightarrow \)  reduced\(\rightarrow \)  lung\(\rightarrow \)  human
111  bacterial resistance to antibacterials  bacterial\(\rightarrow \)  infections  bacterial\(\rightarrow \)  infections\(\rightarrow \)  isolated  bacterial\(\rightarrow \)  pathogens\(\rightarrow \)  identified\(\rightarrow \)  found
112  time studies  total\(\rightarrow \)  number  significant\(\rightarrow \)  increase\(\rightarrow \)  number  ED\(\rightarrow \)  emergency\(\rightarrow \)  visits\(\rightarrow \)  total
113  body weight and metabolism  healthy\(\rightarrow \)  eating  BMI\(\rightarrow \)  obesity\(\rightarrow \)  metabolic  overweight\(\rightarrow \)  BMI\(\rightarrow \)  obesity\(\rightarrow \)  metabolic
114  neo/perinatal infections  included\(\rightarrow \)  among  intrauterine\(\rightarrow \)  transplacental\(\rightarrow \)  flux  intrauterine\(\rightarrow \)  insemination\(\rightarrow \)  doses\(\rightarrow \)  low
115  telemedicine  patient\(\rightarrow \)  care  telemedicine\(\rightarrow \)  visits\(\rightarrow \)  telehealth  telemedicine\(\rightarrow \)  visits\(\rightarrow \)  telehealth\(\rightarrow \)  access
116  coronavirus disease progression  coronavirus\(\rightarrow \)  disease  coronavirus\(\rightarrow \)  disease\(\rightarrow \)  progression  coronavirus\(\rightarrow \)  de\(\rightarrow \)  un\(\rightarrow \)  autre
117  report  reported\(\rightarrow \)  among  reported\(\rightarrow \)  among\(\rightarrow \)  less  reported\(\rightarrow \)  less\(\rightarrow \)  likely\(\rightarrow \)  report
118  severe respiratory syndrome  acute\(\rightarrow \)  respiratory  acute\(\rightarrow \)  respiratory\(\rightarrow \)  syndrome  acute\(\rightarrow \)  respiratory\(\rightarrow \)  syndrome\(\rightarrow \)  coronavirus
119  related works  related\(\rightarrow \)  pandemic  related\(\rightarrow \)  pandemic\(\rightarrow \)  including  status\(\rightarrow \)  related\(\rightarrow \)  pandemic\(\rightarrow \)  including
120  research (publications)  scientific\(\rightarrow \)  research  scientific\(\rightarrow \)  studies\(\rightarrow \)  research  research\(\rightarrow \)  field\(\rightarrow \)  community\(\rightarrow \)  development
Human labels and automated n-gram label proposals based on the proposed heuristic (see Section ). Human labels have been defined by a medical expert by looking (only) to the list of the top 25 words of the topic.
ND stands for not determined.



